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Nguyen QP, Karagas MR, Madan JC, Dade E, Palys TJ, Morrison HG, Pathmasiri WW, McRitche S, Sumner SJ, Frost HR, Hoen AG. Associations between the gut microbiome and metabolome in early life. BMC Microbiol 2021; 21:238. [PMID: 34454437 PMCID: PMC8400760 DOI: 10.1186/s12866-021-02282-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 07/14/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. RESULTS Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. CONCLUSIONS Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.
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Affiliation(s)
- Quang P. Nguyen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
| | - Juliette C. Madan
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
- Department of Pediatrics, Children’s Hospital at Dartmouth, Hanover, NH USA
| | - Erika Dade
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Thomas J. Palys
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Hilary G. Morrison
- Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA USA
| | - Wimal W. Pathmasiri
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Susan McRitche
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Susan J. Sumner
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - H. Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
| | - Anne G. Hoen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Hanover, NH USA
- Children’s Environmental Health & Disease Prevention Research Center at Dartmouth, Lebanon, NH USA
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